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The purpose of this paper is to examine challenges and potential of big data in heterogeneous business networks and relate these to an implemented logistics solution.

The paper establishes an overview of challenges and opportunities of current significance in the area of big data, specifically in the context of transparency and processes in heterogeneous enterprise networks. Within this context, the paper presents how existing components and purpose-driven research were combined for a solution implemented in a nationwide network for less-than-truckload consignments.

Aside from providing an extended overview of today’s big data situation, the findings have shown that technical means and methods available today can comprise a feasible process transparency solution in a large heterogeneous network where legacy practices, reporting lags and incomplete data exist, yet processes are sensitive to inadequate policy changes.

The means introduced in the paper were found to be of utility value in improving process efficiency, transparency and planning in logistics networks. The particular system design choices in the presented solution allow an incremental introduction or evolution of resource handling practices, incorporating existing fragmentary, unstructured or tacit knowledge of experienced personnel into the theoretically founded overall concept.

The paper extends previous high-level view on the potential of big data, and presents new applied research and development results in a logistics application.

This paper aims to study the joint decision making of advance selling and service cancelation for service provides with limited capacity when consumers are overconfident.

For the case in which consumers encounter uncertainties about product valuation and consumption states in the advance period and are overconfident about the probability of a good state, we study how the service provider chooses the optimal sales strategy among the non-advance selling strategy, the advance selling and disallowing cancelation strategy, and the advance selling and allowing cancelation strategy. We also discuss how overconfidence influences the service provider’s decision making.

The results show that when service capacity is sufficient, the service provider should adopt advance selling and disallow cancelation; when service capacity is insufficient, the service provider should still implement advance selling but allow cancelation; and when service capacity is extremely insufficient, the service provider should offer spot sales. Moreover, overconfidence weakens the necessity to allow cancelation under sufficient service capacity and enhances it under insufficient service capacity but is always advantageous to advance selling.

The obtained results provide managerial insights for service providers to make advance selling decisions.

This paper is among the first to explore the effect of consumers’ overconfidence on the joint decision of advance selling and service cancelation under capacity constraints.

Enabled by increased (“big”) data stocks and advanced (“machine learning”) analyses, the concept of human resource analytics (HRA) is expected to systematically improve decisions in human resource management (HRM). Since so far empirical evidence on this is, however, lacking, the authors' study examines which combinations of data and analyses are employed and which combinations deliver on the promise of improved decision quality.

Theoretically, the paper employs a neo-configurational approach for founding and conceptualizing HRA. Methodically, based on a sample of German organizations, two varieties (crisp set and multi-value) of qualitative comparative analysis (QCA) are employed to identify combinations of data and analyses sufficient and necessary for HRA success.

The authors' study identifies existing configurations of data and analyses in HRM and uncovers which of these configurations cause improved decision quality. By evidencing that and which combinations of data and analyses conjuncturally cause decision quality, the authors' study provides a first confirmation of HRA success.

Major limitations refer to the cross-sectional and national sample and the usage of subjective measures. Major implications are the suitability of neo-configurational approaches for future research on HRA, while deeper conceptualizing and researching both the characteristics and outcomes of HRA constitutes a core future task.

The authors' paper employs an innovative theoretical-methodical approach to explain and analyze conditions that conjuncturally cause decision quality therewith offering much needed empirical evidence on HRA success.

This paper aims to examine advance decisions to refuse treatment (ADRTs) in the context of the COVID-19 (Coronavirus 2019) pandemic. This study considers the development of ADRTs, the lack of take up and confusion among the general public, clinicians and health and social care staff.

The paper is a conceptual piece that reflects on ADRTs in the particular context of COVID-19. It considers professional concerns and pronouncements on ADRTs.

ADRTs have a low take up currently. There is misunderstanding among public and professionals. There is a need for raising awareness, developing practice and a need to allay fears of misuse and abuse of ADRTs in clinical, health and social care settings.

The authors make recommendations that reflexive training and awareness become the norm in health and social care, that reform of ADRTs is undertaken to prevent misunderstandings and that the person becomes central in all decision-making processes.

This paper is original in considering ADRTs as a safeguarding issue from two perspectives: that of the person making the ADRT and being confident in respect for the decisions made; and that of clinicians and other professionals being reflexively aware of the need to accept advance decisions and not acting according to unconscious biases in times of crisis.

The greater availability of life‐sustaining technology, such as cardiopulmonary resuscitation, and the medical, legal and moral pressures to use them, often enable the prolongation of lives of older people. The dying process can be extended regardless of quality of life. Further, there is much public debate on the increasing emphasis on individual rights and personal autonomy in the dying process. This qualitative study examined older people's perspectives on end‐of‐life decision‐making and advance care planning. A sample of 12 older people living in the community was recruited and studied in‐depth. A semi‐structured interview explored patients' conceptualisations of decision‐making in the later stages of life and the significant others they would like involved in the process. The data were analysed using ‘content analysis’. The resulting broad categories, themes and sub‐themes formed the foundation of an emerging model of older people talking about end‐of‐life care. Finally, results were discussed with regard to practice and policy development.

Purpose – The purpose of the chapter is to analyze the factors that lead firms to offshore advanced tasks.

Methodology/approach – The study uses a 1,500-firm survey from Denmark to investigate the offshoring of 12 tradable manufacturing, technical, and service activities across different industries.

Findings – Findings indicate that offshoring of advanced tasks is driven by a different set of strategic motives than previous waves of offshoring, which predominantly included simple and standardized routine tasks. While the lower cost of unskilled, labor-intensive processes is the incentive for firms that offshore less advanced tasks, a desire to broaden and deepen global networks of new knowledge spurs highly knowledge-intensive companies to offshore more advanced tasks.

Originality/value of chapter – We propose that offshoring should be analyzed on a more disaggregated level of analysis than is the norm in mainstream offshoring literature. To reflect the trend whereby firms are “slicing” their value chain in finer and finer parts and locate these in various locations around the world, offshoring should be analyzed at the task level, since this paves the way for a richer understanding of offshoring strategies and processes.

The purpose of this paper is to develop a decision tool to help managers make more informed decisions regarding their investments in advanced manufacturing technologies.

Selection of a new technology is multidimensional in nature and interdependent relationships exist among various elements of the system. In addition, both quantitative and qualitative factors need to be considered in the evaluation process. The Analytic Network Process (ANP) methodology satisfies these requirements by considering interdependencies among all the factors and by allowing transformation of qualitative judgments into quantitative values for the decision analysis. These capabilities of ANP make it a perfect methodology for use in the development of our decision tool. Once technology alternatives and selection criteria are identified by the decision maker, several pair‐wise comparisons are conducted to determine local priorities for the selection criteria and alternatives. These priorities are then used to determine the overall priorities for the alternatives. The technology alternative with the highest overall priority is chosen for adoption.

Allowing for interdependencies among selection criteria, as well as between alternatives and selection criteria, provides a more realistic evaluation process than other selection processes that ignore such interdependencies.

The model provides decision makers a tool for evaluating several competitive technology alternatives and selecting the most appropriate technology for adoption.

The paper discusses the inclusion of the subjective judgments of the decision maker in the evaluation process and use of ANP methodology for transforming these judgments into quantitative values for rankings of the alternatives.

The development of low-carbon production is impeded by the investment costs of green technology research and development (R&D) and carbon emission reduction while facing the uncertain risk of emission reduction investment. With the government's carbon emission constraints, green manufacturers implement the advance selling strategy to increase both profit and reduction level. However, few studies consider the consumer's green preference and emission constraints in advance selling market and spot market independently. The authors' paper investigates the optimal strategies of advance selling pricing and reduction effort for green manufacturers to maximize profits.

The authors' paper designs a stochastic model and investigates the manufacturer's optimal strategies of advance selling price and emission reduction efforts by categorizing different purchasing periods of low-carbon consumers. With the challenges of uncertain demand and government's emission constraints, the authors' develop the non-linear optimization model to investigate the manufacturer's profit-oriented decisions.

The results show the government's carbon constraints cannot influence the manufacturer's profit, but the consumer's low-carbon preference in the advance selling period can. Interestingly, the manufacturer will make fewer reduction efforts even when the consumers have stronger environmental awareness. In addition, the increasing consumer price sensitivity will exacerbate the profit loss from mandatory emissions reduction. Overall, for achieving a win–win situation between emission reduction and profit growth, green manufacturers should not only consider the sales strategies, market demand, and government constraints in a low-carbon market, but also pay attention to the uncertainty of green technology innovation.

With the consideration of the government's carbon emission constraints, uncertain demand, and low-carbon consumer's preferences, the authors' study innovatively incorporates the joint impacts of advance selling strategy and emission reduction effort strategy and then differentiates between two cases that pertain to the diverse carbon emission regulations.

The purpose of this paper is to study the advance selling strategies for oligopolists when considering the product diffusion effect.

The authors consider a market that composes of two competitive sellers who are different in their reputation. The two firms sell the same product in the market over two periods (i.e. the advance selling season and the regular selling season). Due to the effect of product diffusion, the demand of each firm in the regular selling season is dependent on the two firms’ advance demands.

For the firm with lower reputation, it is beneficial to decrease the advance selling price with the diffusion effect caused by its advance demand. For the firm with higher reputation, it is also beneficial to decrease the advance selling price with the diffusion effect caused by its advance demand if the consumers’ enthusiasm for the product in regular selling season is high enough; otherwise it should not decrease his advance selling price since this practice cannot greatly increase his demand.

The obtained results can provide operational managers in reality with valuable suggestions in making advance selling decisions.

The paper is among the first to investigate the impact of product diffusion effect on a firm’s advance selling strategy in a competitive setting.